Suchergebnisse

IntroductionMonitoring risk-adjusted trends of neonatal bloodstream infection (BSI) is vital and linkage of neonatal electronic health records to national infection surveillance enables this. We demonstrate why changes in data quality and collection methods over time must be accounted for to minimise spurious findings.
Objectives and ApproachFirst, we determined the effect of a system change in 2014 (changed from only clinically relevant BSI to automated reporting of all BSI), by investigating changes in number of all BSI and BSI excluding the contaminants coagulase-negative staphylococci for infants aged <1 year reported to infection surveillance, using interrupted-time-series Poisson regression. Second, we evaluated the impact of changes in identifier completeness over time in each database, and determined variation in infection rates according to linkage method (deterministic linkage on NHS number or probabilistic linkage). Third, we will use multiple imputation when link status cannot be determined due to missing identifiers.
ResultsThe number of BSI reported to infection surveillance system following the change in data collection increased by 34% (incidence rate ratio (IRR) of 1.34, 95% confidence interval 1.28-1.40) for all BSI compared to 19% (IRR 1.19, 1.12-1.27) excluding coagulase-negative staphylococci. Completeness of NHS number in infection surveillance increased from 69% (3,296/4,792) in 2010 to 92% (3,037/3,307) in 2017. We linked 12,003 neonatal admissions to 15,571 BSI episodes (2% of 497,936 admissions and 41% of 37,660 BSI). The proportion of links that were deterministic changed from 83% (1,089/1,307) in 2010 to 96% (968/1,008) in 2017. There were 12,094 BSI for which the link status could not be determined due to missing identifiers; multiple imputation will be used to determine if any are links.
Conclusion/ImplicationsSpurious trends in infection incidence can arise from changes in data collection and quality, impacting the quality of linkage to clinical data. Data quality and system changes must be explored in each source dataset before analysis. Probabilistic linkage and imputation of missing data minimises spurious findings due to data quality.

Monitoring risk-adjusted trends of neonatal bloodstream infection is vital and linkage of neonatal electronic health records to national infection surveillance enables this. We demonstrate why changes in data quality over time must be accounted for to minimise spurious findings.
First, we evaluated the impact of changes in identifier completeness over time in each database, and determined variation in infection rates according to linkage method (deterministic linkage on NHS number or probabilistic linkage). Second, we will use multiple imputation when link status cannot be determined due to missing identifiers.
Completeness of NHS number in infection surveillance increased from 69% (3,296/4,792) in 2010 to 92% (3,037/3,307) in 2017. We linked 12,003 neonatal admissions to 15,571 infection episodes (2% of 497,936 admissions and 41% of 37,660 infections). The proportion of links that were deterministic changed from 83% (1,089/1,307) in 2010 to 96% (968/1,008) in 2017. Link status could not be determined for 12,094 infections due to missing identifiers; multiple imputation will be used to determine if any are links.
Spurious infection incidence rates can arise from changes in data quality, impacting the quality of linkage to clinical data. Linkage and imputation of missing data minimises spurious findings due to data quality.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Download ; Investments to reduce the spread of antimicrobial resistance (AMR) in the European Union have been made, including efforts to strengthen prudent antimicrobial use. Using segmented regression, we report decreasing and stabilising trends in data reported to the European Surveillance of Antimicrobial Consumption Network and stabilising trends in data reported to the European Antimicrobial Resistance Surveillance Network. Our results could be an early indication of the effect of prioritising AMR on the public health agenda. ; El Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica 2013-2016 Instituto de Salud Carlos III, Subdireccion General de Redes y Centros de Investigacion Cooperativa, Ministerio de Economia, Industria y Competitividad, Spanish Network for Research in Infectious Diseases European Development Regional Fund "A way to achieve Europe", Operative program Intelligent Growth 2014-2020 Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC)